Adaptive Task and Resource Allocation Networks

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Abstract

In this study I introduce a new approach to the task allocation problem. This
is the problem of finding the optimal way to allocate tasks to agents in a given
environment. In this model, each agent has a set of traits which determines their
ability to solve any given task. Messages can then be passed between agents which
are the interface for task exchange. Thus through communication with other
agents, tasks are passed from one agent to another, and connections are formed.
Agents are then able to use this connection network to pass tasks more quickly
between one another. As connections form, agents can pass messages between
themselves in order to find the best way to solve all the tasks in the system.
Preliminary results show that there is an optimal level of connectedness for this
network in which performance peaks. I will also show that there is an optimal
amount of time that an agent should work on a task it needs to solve before
attending to its connection network. This is because if an agent spends too much
time processing messages, no work will be done on the actual tasks it needs to
solve, and performance will decrease. In the same respect, if an agent were to
spend too little time processing messages, it would not get messages about new
tasks it needs to solve, and it would not be able to send out messages as often
about the tasks it needs help solving, and thus performance would also decrease.
This has many applications to real world systems, such as large companies and
grid computing where many tasks need to be allocated to many different nodes and
communication always has a cost. There are also many other questions that this
research is investigating, such as how much work does each agent do on average.
That is, do some agents do all the work while others sit idle and waste resources?
What network structures will yield the best performance of the system and why?
These are the types of questions which the following model can help answer.